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Found 10 papers, 5 papers with code
Open Source Handwritten Text Recognition on Medieval Manuscripts using Mixed Models and Document-Specific Finetuning
We report on our efforts to construct mixed recognition models which can be applied out-of-the-box without any further document-specific training but also serve as a starting point for finetuning by training a new model on a few pages of transcribed text (ground truth).
Mixed Model OCR Training on Historical Latin Script for Out-of-the-Box Recognition and Finetuning
Training a more specialized model for some unseen Early Modern Latin books starting from our mixed model led to a CER of 1. 47%, an improvement of up to 50% compared to training from scratch and up to 30% compared to training from the aforementioned standard model.
OCR4all -- An Open-Source Tool Providing a (Semi-)Automatic OCR Workflow for Historical Printings
Nevertheless, in the last few years great progress has been made in the area of historical OCR, resulting in several powerful open-source tools for preprocessing, layout recognition and segmentation, character recognition and post-processing.
Optical Character Recognition
Optical Character Recognition (OCR)
State of the Art Optical Character Recognition of 19th Century Fraktur Scripts using Open Source Engines
In this paper we evaluate Optical Character Recognition (OCR) of 19th century Fraktur scripts without book-specific training using mixed models, i. e. models trained to recognize a variety of fonts and typesets from previously unseen sources.
Optical Character Recognition
Optical Character Recognition (OCR)
Ground Truth for training OCR engines on historical documents in German Fraktur and Early Modern Latin
In this paper we describe a dataset of German and Latin \textit{ground truth} (GT) for historical OCR in the form of printed text line images paired with their transcription.
Improving OCR Accuracy on Early Printed Books by combining Pretraining, Voting, and Active Learning
We combine three methods which significantly improve the OCR accuracy of OCR models trained on early printed books: (1) The pretraining method utilizes the information stored in already existing models trained on a variety of typesets (mixed models) instead of starting the training from scratch.
Transfer Learning for OCRopus Model Training on Early Printed Books
The evaluation on seven early printed books showed that training from the Latin mixed model reduces the average amount of errors by 43% and 26%, respectively compared to training from scratch with 60 and 150 lines of ground truth, respectively.
Improving OCR Accuracy on Early Printed Books by utilizing Cross Fold Training and Voting
Experiments on seven early printed books show that the proposed method outperforms the standard approach considerably by reducing the amount of errors by up to 50% and more.
LAREX - A semi-automatic open-source Tool for Layout Analysis and Region Extraction on Early Printed Books
A semi-automatic open-source tool for layout analysis on early printed books is presented.
Profiling of OCR'ed Historical Texts Revisited
Here we improve this method in three respects: First, the method in Reffle (2013) is not adaptive: user feedback obtained by actual postcorrection steps cannot be used to compute refined profiles.
